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Journal of Structural Engineering and Management Review Article

Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review

  1. Ritesh Vishwakarma Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, University of Mumbai
  2. Karthik Nagarajan Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, University of Mumbai
  3. Raju Narwade Department of Civil Engineering, Pillai HOC College of Engineering and Technology, Rasayani, University of Mumbai

Abstract

Structural health monitoring (SHM) has a critical role in ensuring civil infrastructure safety, reliability, and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast amounts of data for structural inspection, but do not effectively manage complicated nonlinear data. This research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Methods,” investigates the feasibility of the optimization of SHM performance by employing artificial neural networks (ANN) for improved interpretation of data, accuracy of prediction, and decision-making in maintenance. The research process constituted extensive literature review, bridge modeling as a simulation platform, and experimentation with ANN models like feedforward, convolutional, and recurrent networks. ANN enables efficient analysis of sensor outputs, pattern recognition of damage, and prediction of damage growth with increased accuracy in structural diagnosis. Comparative investigation with conventional SHM methods verifies that ANN-based systems exhibit better computational efficiency, accuracy, and real-time performance. But needs such as data quality demands, interpretability of the model, and computationally intensive analysis remain. The results highlight that the integration of ANN makes SHM an intelligent, dynamic, and futuristic system, and thus a leap and bound improvement in the digitalization of urban infrastructure. Smarter decision-making and predictive maintenance by ANNbased SHM enhance safety considerably, cost savings are realized, and smart city sustainability is enhanced.

Keywords

References (39)

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